mirror of
https://github.com/RapidAI/RapidOCR.git
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294 lines
10 KiB
Python
294 lines
10 KiB
Python
# !/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import argparse
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import copy
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import imghdr
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import math
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import random
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from pathlib import Path
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import cv2
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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def get_image_file_list(img_file):
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if img_file is None or not Path(img_file).exists():
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raise FileExistsError(f"not found any img file in {img_file}")
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imgs_lists = []
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img_end = ['jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif']
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def _get_img_path(img_file):
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# 真实后缀
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real_img_suffix = imghdr.what(img_file)
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# 现在后缀
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now_img_suffix = img_file.suffix[1:].lower()
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if real_img_suffix in img_end:
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# 真实后缀与现有后缀不一致
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if now_img_suffix in img_end:
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return img_file
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else:
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# 按照真实后缀重命名
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new_img_file = img_file.with_suffix(f'.{real_img_suffix}')
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img_file.rename(new_img_file)
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return new_img_file
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else:
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return None
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img_file = Path(img_file)
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if img_file.is_file():
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tmp_img_path = _get_img_path(img_file)
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if tmp_img_path is not None:
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imgs_lists.append(str(tmp_img_path))
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elif img_file.is_dir():
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for img_path in img_file.iterdir():
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tmp_file_path = _get_img_path(img_path)
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if tmp_file_path is not None:
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imgs_lists.append(str(tmp_file_path))
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else:
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raise ValueError(f"The {img_file}'s format is not support!")
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if len(imgs_lists) == 0:
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raise FileExistsError(f"not found any img file in {img_file}")
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return imgs_lists
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def check_and_read_gif(img_path):
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if Path(img_path).name[-3:] in ['gif', 'GIF']:
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gif = cv2.VideoCapture(img_path)
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ret, frame = gif.read()
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if not ret:
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print("Cannot read {}. This gif image maybe corrupted.")
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return None, False
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if len(frame.shape) == 2 or frame.shape[-1] == 1:
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frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
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imgvalue = frame[:, :, ::-1]
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return imgvalue, True
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return None, False
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def draw_ocr_box_txt(image, boxes, txts,
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scores=None, text_score=0.5,
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font_path="./models/msyh.ttc"):
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h, w = image.height, image.width
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img_left = image.copy()
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img_right = Image.new('RGB', (w, h), (255, 255, 255))
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random.seed(0)
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draw_left = ImageDraw.Draw(img_left)
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draw_right = ImageDraw.Draw(img_right)
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for idx, (box, txt) in enumerate(zip(boxes, txts)):
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if scores is not None and scores[idx] < text_score:
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continue
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color = (random.randint(0, 255),
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random.randint(0, 255),
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random.randint(0, 255))
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draw_left.polygon(box, fill=color)
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draw_right.polygon([box[0][0], box[0][1],
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box[1][0], box[1][1],
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box[2][0], box[2][1],
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box[3][0], box[3][1]],
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outline=color)
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box_height = math.sqrt((box[0][0] - box[3][0])**2 \
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+ (box[0][1] - box[3][1])**2)
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box_width = math.sqrt((box[0][0] - box[1][0])**2 \
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+ (box[0][1] - box[1][1])**2)
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if box_height > 2 * box_width:
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font_size = max(int(box_width * 0.9), 10)
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font = ImageFont.truetype(font_path, font_size,
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encoding="utf-8")
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cur_y = box[0][1]
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for c in txt:
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char_size = font.getsize(c)
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draw_right.text((box[0][0] + 3, cur_y), c,
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fill=(0, 0, 0), font=font)
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cur_y += char_size[1]
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else:
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font_size = max(int(box_height * 0.8), 10)
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font = ImageFont.truetype(font_path, font_size,
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encoding="utf-8")
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draw_right.text([box[0][0], box[0][1]], txt,
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fill=(0, 0, 0), font=font)
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img_left = Image.blend(image, img_left, 0.5)
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img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
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img_show.paste(img_left, (0, 0, w, h))
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img_show.paste(img_right, (w, 0, w * 2, h))
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return np.array(img_show)
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def visualize(image_path, boxes, rec_res):
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image = Image.open(image_path)
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txts = [rec_res[i][0] for i in range(len(rec_res))]
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scores = [rec_res[i][1] for i in range(len(rec_res))]
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draw_img = draw_ocr_box_txt(image, boxes,
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txts, scores,
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text_score=0.5)
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draw_img_save = Path("./inference_results/")
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if not draw_img_save.exists():
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draw_img_save.mkdir(parents=True, exist_ok=True)
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image_save = str(draw_img_save / f'infer_{Path(image_path).name}')
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cv2.imwrite(image_save, draw_img[:, :, ::-1])
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print(f'The infer result has saved in {image_save}')
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class TextSystem(object):
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def __init__(self, det_model_path,
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rec_model_path,
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use_angle_cls=False,
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cls_model_path=None) -> None:
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super(TextSystem).__init__()
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self.text_detector = TextDetector(det_model_path)
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self.text_recognizer = TextRecognizer(rec_model_path)
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self.use_angle_cls = use_angle_cls
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if self.use_angle_cls:
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self.text_classifier = TextClassifier(cls_model_path)
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def get_rotate_crop_image(self, img, points):
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'''
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img_height, img_width = img.shape[0:2]
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left = int(np.min(points[:, 0]))
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right = int(np.max(points[:, 0]))
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top = int(np.min(points[:, 1]))
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bottom = int(np.max(points[:, 1]))
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img_crop = img[top:bottom, left:right, :].copy()
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points[:, 0] = points[:, 0] - left
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points[:, 1] = points[:, 1] - top
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'''
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img_crop_width = int(
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max(
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np.linalg.norm(points[0] - points[1]),
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np.linalg.norm(points[2] - points[3])))
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img_crop_height = int(
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max(
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np.linalg.norm(points[0] - points[3]),
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np.linalg.norm(points[1] - points[2])))
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pts_std = np.float32([[0, 0], [img_crop_width, 0],
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[img_crop_width, img_crop_height],
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[0, img_crop_height]])
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M = cv2.getPerspectiveTransform(points, pts_std)
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dst_img = cv2.warpPerspective(
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img,
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M, (img_crop_width, img_crop_height),
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borderMode=cv2.BORDER_REPLICATE,
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flags=cv2.INTER_CUBIC)
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dst_img_height, dst_img_width = dst_img.shape[0:2]
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if dst_img_height * 1.0 / dst_img_width >= 1.5:
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dst_img = np.rot90(dst_img)
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return dst_img
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@staticmethod
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def sorted_boxes(dt_boxes):
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"""
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Sort text boxes in order from top to bottom, left to right
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args:
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dt_boxes(array):detected text boxes with shape [4, 2]
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return:
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sorted boxes(array) with shape [4, 2]
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"""
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num_boxes = dt_boxes.shape[0]
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sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
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_boxes = list(sorted_boxes)
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for i in range(num_boxes - 1):
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if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
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(_boxes[i + 1][0][0] < _boxes[i][0][0]):
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tmp = _boxes[i]
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_boxes[i] = _boxes[i + 1]
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_boxes[i + 1] = tmp
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return _boxes
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@staticmethod
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def load_image(image_path):
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img, flag = check_and_read_gif(image_path)
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if not flag:
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img = cv2.imread(image_path)
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if img is None:
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raise ValueError(f"error in loading image:{image_path}")
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return img
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def __call__(self, image_path):
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img = self.load_image(image_path)
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dt_boxes, elapse = self.text_detector(img)
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print("dt_boxes num : {}, elapse : {}".format(
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len(dt_boxes), elapse))
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if dt_boxes is None or len(dt_boxes) < 1:
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return None, None
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dt_boxes = self.sorted_boxes(dt_boxes)
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img_crop_list = []
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for bno in range(len(dt_boxes)):
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tmp_box = copy.deepcopy(dt_boxes[bno])
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img_crop = self.get_rotate_crop_image(img, tmp_box)
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img_crop_list.append(img_crop)
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if self.use_angle_cls:
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img_crop_list, angle_list, elapse = self.text_classifier(
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img_crop_list)
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print("cls num : {}, elapse : {}".format(
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len(img_crop_list), elapse))
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rec_res, elapse = self.text_recognizer(img_crop_list)
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print("rec_res num : {}, elapse : {}".format(
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len(rec_res), elapse))
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filter_boxes, filter_rec_res = [], []
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for box, rec_reuslt in zip(dt_boxes, rec_res):
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text, score = rec_reuslt
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if score >= text_score:
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filter_boxes.append(box)
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filter_rec_res.append(rec_reuslt)
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return filter_boxes, filter_rec_res
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--det_model_path', type=str,
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default='models/ch_ppocr_mobile_v2.0_det_infer.onnx')
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parser.add_argument('--cls_model_path', type=str,
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default='models/ch_ppocr_mobile_v2.0_cls_infer.onnx')
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parser.add_argument('--rec_model_path', type=str,
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default='models/ch_ppocr_mobile_v2.0_rec_infer.onnx')
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parser.add_argument('--image_path', type=str,
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default='test_images/det_images/')
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parser.add_argument('--text_score', type=float, default=0.5)
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args = parser.parse_args()
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text_score = args.text_score
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from ch_ppocr_mobile_v2_cls import TextClassifier
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if args.det_model_path.find('server') != -1:
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from ch_ppocr_server_v2_det import TextDetector
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else:
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from ch_ppocr_mobile_v2_det import TextDetector
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if args.rec_model_path.find('server') != -1:
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from ch_ppocr_server_v2_rec import TextRecognizer
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else:
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from ch_ppocr_mobile_v2_rec import TextRecognizer
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text_sys = TextSystem(args.det_model_path,
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args.rec_model_path,
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use_angle_cls=True,
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cls_model_path=args.cls_model_path)
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image_file_list = get_image_file_list(args.image_path)
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for image_path in image_file_list:
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dt_boxes, rec_res = text_sys(image_path)
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visualize(image_path, dt_boxes, rec_res)
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print('=' * 10)
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